Built to production standards
Years keeping Amazon's systems up shape how everything here is built — tested, fail-closed, security-hardened. Not a weekend prototype that breaks in front of a customer.
About One operator, end to end
Custom AI, built and run by the person you speak to.
No juniors, no offshore, no account layer. The person who scopes your build is the person who writes the code and keeps it running.
Operator-led deliveryKaylum — founder, Inbound AI
The operator
Hi, I'm Kaylum.
I'm a network engineer with six years in the field — three of them keeping infrastructure reliable at Amazon — now building agentic AI to the same standard. I started Inbound AI because most businesses new to AI get sold hype or a half-finished pilot. I'd rather find where AI actually pays, build the system properly, and run it. I scope, build and operate every engagement myself.
- Spec → plan → adversarial audit → build. Every release is red-teamed by independent passes before it ships — that's how bugs get caught before you ever see them.
- Evidence-first AI. My systems won't state a fact they can't back with a source — the reason Broker Book Engine runs at 0% invented facts on its eval set.
- Production-grade, not prototypes. Hundreds of automated tests, fail-closed logic, tenant isolation and security hardening — SEO Commander, SyncPostr and Broker Book Engine all run live.
- Build + run. I operate what I build, in production, every day — so it keeps working long after launch.
The practice
What Inbound AI stands on.
AI that won't make things up
Every system has a verify step: it won't state a fact it can't back with a source. That's the difference between a demo and something you can trust with customers.
"Don't automate that yet"
If AI won't pay for itself in your business, you'll hear it on the first call — before you've spent anything. The disqualifier is the point.
See where AI would pay in your business.
A free 30-minute call. We'll map it honestly — and if it's not worth building, we'll tell you.